Airborne particulate matter, population mobility and COVID-19: a multi-city study in China

Posted on 22.10.2020 - 03:34
Abstract Background Coronavirus disease 2019 (COVID-19) is an emerging infectious disease, which has caused numerous deaths and health problems worldwide. This study aims to examine the effects of airborne particulate matter (PM) pollution and population mobility on COVID-19 across China. Methods We obtained daily confirmed cases of COVID-19, air particulate matter (PM2.5, PM10), weather parameters such as ambient temperature (AT) and absolute humidity (AH), and population mobility scale index (MSI) in 63 cities of China on a daily basis (excluding Wuhan) from January 01 to March 02, 2020. Then, the Generalized additive models (GAM) with a quasi-Poisson distribution were fitted to estimate the effects of PM10, PM2.5 and MSI on daily confirmed COVID-19 cases. Results We found each 1 unit increase in daily MSI was significantly positively associated with daily confirmed cases of COVID-19 in all lag days and the strongest estimated RR (1.21, 95% CIs:1.14 ~ 1.28) was observed at lag 014. In PM analysis, we found each 10 μg/m3 increase in the concentration of PM10 and PM2.5 was positively associated with the confirmed cases of COVID-19, and the estimated strongest RRs (both at lag 7) were 1.05 (95% CIs: 1.04, 1.07) and 1.06 (95% CIs: 1.04, 1.07), respectively. A similar trend was also found in all cumulative lag periods (from lag 01 to lag 014). The strongest effects for both PM10 and PM2.5 were at lag 014, and the RRs of each 10 μg/m3 increase were 1.18 (95% CIs:1.14, 1.22) and 1.23 (95% CIs:1.18, 1.29), respectively. Conclusions Population mobility and airborne particulate matter may be associated with an increased risk of COVID-19 transmission.


Wang, Bo; Liu, Jiangtao; Li, Yanlin; Fu, Shihua; Xu, Xiaocheng; Li, Lanyu; et al. (2020): Airborne particulate matter, population mobility and COVID-19: a multi-city study in China. figshare. Collection.
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BMC Public Health


Bo Wang
Jiangtao Liu
Yanlin Li
Shihua Fu
Xiaocheng Xu
Lanyu Li
Ji Zhou
Xingrong Liu
Xiaotao He
Jun Yan
Yanjun Shi
Jingping Niu
Yong Yang
Yiyao Li
Bin Luo
Kai Zhang
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